import torch
# Download an example image from the pytorch website
import urllib
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
from torchsummary import summary
import os

if __name__ == '__main__':
    model = torch.hub.load('pytorch/vision:v0.6.0', 'fcn_resnet101', pretrained=True)
    model.eval()

    url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg")
    if not os.path.exists(filename):
        try:
            urllib.URLopener().retrieve(url, filename)
        except:
            urllib.request.urlretrieve(url, filename)

    input_image = Image.open(filename)
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')

    with torch.no_grad():
        output = model(input_batch)['out'][0]
    output_predictions = output.argmax(0)

    # create a color pallette, selecting a color for each class
    palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
    colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
    colors = (colors % 255).numpy().astype("uint8")

    # plot the semantic segmentation predictions of 21 classes in each color
    r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
    r.putpalette(colors)

    import matplotlib.pyplot as plt

    plt.imshow(r)
    plt.show()
